Editing IPCC:Wg1:Chapter:Chapter-1-comments (section)

==== 1.5.3.1 Earth System Models ====

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Earth system models are mathematical formulations of the natural laws that govern the evolution of climate-relevant systems: atmosphere, ocean, cryosphere, land, and biosphere, as well as the carbon cycle ( [[#Flato--2011|Flato, 2011]] ). They build on the fundamental laws of physics (e.g., Navier–Stokes or Clausius–Clapeyron equations) or empirical relationships established from observations and, when possible, they are constrained by fundamental conservation laws (e.g., mass and energy). The evolution of climate-relevant variables is computed numerically using high-performance computers ( [[#André--2014|André et al., 2014]] ; [[#Balaji--2017|Balaji et al., 2017]] ), on three-dimensional discrete grids ( [[#Staniforth--2012|Staniforth and Thuburn, 2012]] ). The spatial (and temporal) resolution of these grids in both the horizontal and vertical directions determines which processes need to be parameterized or whether they can be explicitly resolved. Developments since AR5 in model resolution, parameterizations and modelling of the land and ocean biosphere and of biogeochemical cycles are discussed below.

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===== ''1.5.3.1.1 Model grids'' ''and resolution'' =====

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The horizontal resolution and the number of vertical levels in ESMs is generally higher in CMIP6 than in CMIP5 (Figure 1.19). Global models with finer horizontal grids better represent many aspects of the circulation of the atmosphere ( [[#Gao--2020|Gao et al., 2020]] ; [[#Schiemann--2020|Schiemann et al., 2020]] ) and ocean ( [[#Bishop--2016|Bishop et al., 2016]] ; [[#Storkey--2018|Storkey et al., 2018]] ), bringing improvements in the simulation of the global hydrological cycle ( [[#Roberts--2018|Roberts et al., 2018]] ). CMIP6 includes a dedicated effort (HighResMIP, [[#Haarsma--2016|Haarsma et al., 2016]] ) to explore the effect of higher horizontal resolution, such as ~50 km, ~25 km and even ~10 km ( [[#1.5.4.2|Section 1.5.4.2]] and Annex II, Table AII.6). Improvements are documented in the highest-resolution coupled models used for HighResMip ( [[#Hewitt--2017|]] [[#Hewitt--2017|Hewitt et al., 2017]] ; [[#Roberts--2019|Roberts et al., 2019]] ). Flexible grids allowing spatially variable resolution in the atmosphere ( [[#McGregor--2015|McGregor, 2015]] ; [[#Giorgetta--2018|Giorgetta et al., 2018]] ) and in the ocean ( [[#Wang--2014|Wang et al., 2014]] ; [[#Petersen--2019|Petersen et al., 2019]] ) are more widely used than at the time of the AR5.

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'''Figure 1.19 |''' '''Resolution of the atmospheric and oceanic components of global climate models participating in CMIP5, CMIP6 and HighResMIP:''' '''(a, b)''' horizontal resolution (km), and '''(c, d)''' number of vertical levels. Darker-colour circles indicate high-top models (in which the top of the atmosphere is above 50 km). The crosses are the median values. These models are documented in Annex II. Note that duplicated models in a modelling group are counted as one entry when their horizontal and vertical resolutions are the same. For HighResMIP, one atmosphere–ocean coupled model with the highest resolution from each modelling group is used. The horizontal resolution (rounded to 10 km) is the square root of the surface area of the Earth divided by the number of grid points, or the area of the ocean surface divided by the number of surface ocean grid points, for the atmosphere and ocean, respectively.
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The number of vertical levels in the atmosphere of global models has increased (Figure 1.19), partly to enable simulations to include higher levels in the atmosphere and better represent stratospheric processes ( [[#Charlton-Perez--2013|Charlton-Perez et al., 2013]] ; [[#Kawatani--2019|Kawatani et al., 2019]] ). Half the modelling groups now use ‘high-top’ models with a top level above the stratopause (a pressure of about 1 hPa). The number of vertical levels in the ocean models has also increased in order to achieve finer resolution over the water column and especially in the upper mixed layer and to better resolve the diurnal cycle ( [[IPCC:Wg1:Chapter:Chapter-3#3.5|Section 3.5]] and Annex II; [[#Bernie--2008|Bernie et al., 2008]] ).

Despite the documented progress of higher resolution, the model evaluation carried out in subsequent chapters shows that improvements between CMIP5 and CMIP6 remain modest at the global scale ( [[IPCC:Wg1:Chapter:Chapter-3#3.8.2|Section 3.8.2]] ; [[#Bock--2020|Bock et al., 2020]] ). Lower resolution alone does not explain all model biases, for example, a low blocking frequency ( [[#Davini--2020|Davini and D’Andrea, 2020]] ) or a wrong shape of the Intertropical Convergence Zone ( [[#Tian--2020|Tian and Dong, 2020]] ). Model performance depends on model formulation and parameterizations as much as on resolution (Chapters 3, 8 and 10).

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===== 1.5.3.1.2 Representation of physical and chemical processes in ESMs =====

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Atmospheric models include representations of physical processes such as clouds, turbulence, convection and gravity waves that are not fully represented by grid-scale dynamics. The CMIP6 models have undergone updates in some of their parameterization schemes compared to their CMIP5 counterparts, with the aim of better representing the physics and bringing the climatology of the models closer to newly available observational datasets. Most notable developments are to schemes involving radiative transfer, cloud microphysics, and aerosols, in particular a more explicit representation of the aerosol indirect effects through aerosol-induced modification of cloud properties. Broadly, aerosol–cloud microphysics has been a key topic for the aerosol and chemistry modelling communities since AR5, leading to improved understanding of the climate influence of short-lived climate forcers, but they remain the single largest source of spread in ESM calculations of climate sensitivity ( [[#Meehl--2020|Meehl et al., 2020]] ), with numerous parameterization schemes in use (Section 6.4; [[#Gettelman--2016|Gettelman and Sherwood, 2016]] ; [[#Zhao--2018|Zhao et al., 2018]] ; [[#Gettelman--2019|Gettelman et al., 2019]] ). The treatment of droplet size and mixed-phase clouds (liquid and ice) was found to lead to changes in the climate sensitivity (Glossary) of some models between AR5 and AR6 (Section 7.4; [[#Bodas-Salcedo--2019|Bodas-Salcedo et al., 2019]] ; [[#Gettelman--2019|Gettelman et al., 2019]] ; [[#Zelinka--2020|Zelinka et al., 2020]] ).

The representation of ocean and cryosphere processes has also evolved significantly since CMIP5. The explicit representation of ocean eddies, due to increased grid resolution (typically, from 1° to ¼°), is a major advance in a number of CMIP6 ocean model components ( [[#Hewitt--2017|]] [[#Hewitt--2017|Hewitt et al., 2017]] ). Advances in sea ice models have been made, for example through correcting known shortcomings in CMIP5 simulations, in particular the persistent underestimation of the rapid decline in summer Arctic sea ice extent ( [[#Rosenblum--2016|Rosenblum and Eisenman, 2016]] , 2017; [[#Turner--2017|Turner and Comiso, 2017]] ; [[#Notz--2018|Notz and Stroeve, 2018]] ). The development of glacier and ice-sheet models has been motivated and guided by an improved understanding of key physical processes, including grounding line dynamics, stratigraphy and microstructure evolution, sub-shelf melting, and glacier and ice-shelf calving, among others ( [[#Faria--2014|Faria et al., 2014]] , 2018; [[#Hanna--2020|Hanna et al., 2020]] ). The resolution of ice-sheet models has continuously increased, including the use of nested grids, sub-grid interpolation schemes, and adaptive mesh approaches ( [[#Cornford--2016|Cornford et al., 2016]] ), mainly for a more accurate representation of grounding-line migration and data assimilation ( [[#Pattyn--2018|Pattyn, 2018]] ). Ice-sheet models are increasingly interactively coupled with global and regional climate models, accounting for the height–mass-balance feedback ( [[#Vizcaino--2015|Vizcaino et al., 2015]] ; [[#Le%20clec’h--2019|Le clec’h et al., 2019]] ), and enabling a better representation of ice-ocean processes, in particular for the Antarctic Ice Sheet ( [[#Asay-Davis--2017|Asay-Davis et al., 2017]] ).

Sealevel rise is caused by multiple processes acting on multiple time scales: ocean warming, glaciers and ice-sheet melting, change in water storage on land, and glacial isostatic adjustment (Box 9.1) but no single model can represent all these processes (Section 9.6). In this Report, the contributions are computed separately (Figure 9.28) and merged into a common probabilistic framework and updated from AR5 (Section 9.6; [[#Church--2013|Church et al., 2013]] ; [[#Kopp--2014|Kopp et al., 2014]] ).

Another notable development since AR5 is the inclusion of stochastic parameterizations of sub-grid processes in some comprehensive climate models ( [[#Sanchez--2016|Sanchez et al., 2016]] ). Here, the deterministic differential equations that govern the dynamical evolution of the model are complemented by knowledge of the stochastic variability in unresolved processes. While not yet widely implemented, the approach has been shown to improve the forecasting skill of weather models, to reduce systematic biases in global models ( [[#Berner--2017|Berner et al., 2017]] ; [[#Palmer--2019|Palmer, 2019]] ) and to influence simulated climate sensitivity ( [[#Strommen--2019|Strommen et al., 2019]] ).

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===== 1.5.3.1.3 Representation of biogeochemistry, including the carbon cycle =====

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Since AR5, more sophisticated land-use and land-cover change representations in ESMs have been developed to simulate the effects of land management on surface fluxes of carbon, water and energy ( [[#Lawrence--2016|Lawrence et al., 2016]] ), although the integration of many processes (e.g., wetland drainage, fire as a management tool) remains a challenge ( [[#Pongratz--2018|Pongratz et al., 2018]] ). The importance of nitrogen availability to limit the terrestrial carbon sequestration has been recognized (Section 5.4; [[#Zaehle--2014|Zaehle et al., 2014]] ) and so an increasing number of models now include a prognostic representation of the terrestrial nitrogen cycle and its coupling to the land carbon cycle ( [[#Jones--2016|Jones et al., 2016]] ; [[#Arora--2020|Arora et al., 2020]] ), leading to a reduction in uncertainty for carbon budgets (Section 5.1; [[#Jones--2020|Jones and Friedlingstein, 2020]] ). As was the case in CMIP5 ( [[#Ciais--2013|Ciais et al., 2013]] ), the land surface processes represented vary across CMIP6 models, with at least some key processes (fire, permafrost carbon, microbes, nutrients, vegetation dynamics, plant demography) absent from any particular ESM land model (Table 5.4). Ocean biogeochemical models have evolved to enhance the consistency of the exchanges between ocean, atmosphere and land, through riverine input and dust deposition ( [[#Stock--2014|Stock et al., 2014]] ; [[#Aumont--2015|Aumont et al., 2015]] ). Other developments include flexible plankton stoichiometric ratios ( [[#Galbraith--2015|Galbraith and Martiny, 2015]] ), improvements in the representation of nitrogen fixation ( [[#Paulsen--2017|Paulsen et al., 2017]] ), and the limitation of plankton growth by iron ( [[#Aumont--2015|Aumont et al., 2015]] ). Due to the long time scale of biogeochemical processes, how the models are initialized (spun up) strategies has been shown to affect their performance in AR5 ( [[#Séférian--2016|Séférian et al., 2016]] ).

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